Detection of Stress Using Image Processing and Machine Learning Techniques

Abstract

Stress is a part of life it is an unpleasant state of emotional arousal that people experience in situations like working for long hours in front of computer. Computers have become a way of life, much life is spent on the computers and hence we are therefore more affected by the ups and downs that they cause us. One cannot just completely avoid their work on computers but one can at least control his/her usage when being alarmed about him being stressed at certain point of time. Monitoring the emotional status of a person who is working in front of a computer for longer duration is crucial for the safety of a person. In this work a real-time non-intrusive videos are captured, which detects the emotional status of a person by analysing the facial expression. We detect an individual emotion in each video frame and the decision on the stress level is made in sequential hours of the video captured. We employ a technique that allows us to train a model and analyze differences in predicting the features. Theano is a python framework which aims at improving both the execution time and development time of the linear regression model which is used here as a deep learning algorithm. The experimental results show that the developed system is well on data with the generic model of all ages. Keyword-Stress, Facial expression, Theano, Framework, Deep learning I. INTRODUCTION Most of the researchers focused on detecting stress involved in a person, which causes in a person several emotional problems like anxiety, grief, low self-esteem and other mental health problems. Recent studies have shown that stress can also affect the aspects of your life, including your thinking ability and physical health. To reduce riskiness from being stress and affected with its adverse effects, it is crucial to detect such emotions and take certain actions to relax them. Most of the previous work on stress detection is based on the digital signal processing, taking into consideration Galvanic skin response, blood volume, pupil dilation and skin temperature. And the other work on this issue is based on several physiological signals and visual features (eye closure, head movement) to monitor the stress in a person while he is working. However these measurements are intrusive and are less comfortable in real application. In this work we develop a stress detection system based on the analysis of the facial expression. The system is non-intrusive and is able to run in real-time. A camera is used to capture the near frontal view of the person while he is working in front of the computer. The camera is mounted facing a person. Video captured is divided into three sections of equal length and set of equal number of image frames are extracted from each section correspondingly and are analysed. The image analysis includes the calculation of the variation in the position of the eyebrow from its mean position. The displacement of eyebrow from its position is calculated by scanning the image for the eyebrow coordinates. If the person is found stressed in the consecutive sections of the time intervals which was previously divided, the decision for stress detection is made for a person working in front of the computer With the obtained results we employ the technique of deep learning which is a branch of machine learning which gives the computer an ability to learn without being explicitly programmed. Theano is a python framework which aims at improving both the execution time and development time of the linear regression model which is used here as a deep learning algorithm.